Cone-beam computed tomography-based radiomic analysis of architectural phenotypes in jaw cysts and tumors using interpretable artificial intelligence models

Abstract

BACKGROUND

Jaw lesions, like cysts and tumors, demonstrate substantial variation in internal architectural organization, spatial heterogeneity, and voxel-level complexity on cone-beam computed tomography (CBCT). Conventional radiological interpretation relies predominantly on subjective visual assessment and may not adequately capture these underlying imaging phenotypes.

AIM

To evaluate whether CBCT-derived radiomic features can quantitatively characterize architectural phenotypes of jaw lesions and to assess their discrimination using interpretable artificial intelligence (AI) models.

METHODS

This retrospective study analyzed 100 histopathologically confirmed jaw lesions using CBCT. Lesions were manually segmented using 3D Slicer and 107 radiomic features were extracted after standardized preprocessing and voxel normalization using PyRadiomics. Lesions were classified into homogeneous fluid-dominant, intermediate septated, and complex heterogeneous phenotypes. Feature stability was assessed using intraclass correlation coefficients, while selection employed false discovery rate (FDR) correction, correlation pruning, and LASSO regression. Logistic regression (LR), support vector machine (SVM), and random forest (RF) models underwent stratified five-fold cross-validation and independent chronological validation.

RESULTS

Forty radiomic features demonstrated statistically significant differences among architectural phenotypic groups following FDR correction. Feature reduction yielded a compact radiomic signature predominantly composed of texture-derived descriptors reflecting gray-level non-uniformity, spatial dependence variability, entropy, and structural complexity. The LR model demonstrated the highest performance, achieving an area under the receiver operating characteristic curve of 0.92, with robust discrimination between architectural phenotypes. SVM and RF models demonstrated comparable but lower performance. Lesions categorized within the complex heterogeneous phenotype exhibited significantly elevated texture heterogeneity metrics compared with homogeneous fluid-dominant lesions, supporting the biological relevance of radiomic architectural characterization in differentiating complex jaw pathologies.

CONCLUSION

CBCT-derived radiomic features enable quantitative assessment of internal architectural phenotypes in jaw lesions, particularly patterns related to spatial heterogeneity and structural organization. Texture-based radiomic signatures, when integrated with interpretable AI models, may function as imaging biomarkers for objective lesion characterization and may support future development of biologically informed diagnostic decision-support systems in oral and maxillofacial radiology.

Keywords: Artificial intelligence; Cone-beam computed tomography; Jaw cysts; Jaw Tumors; Radiomics

Core Tip: Radiomic analysis of cone-beam computed tomographic images allows objective evaluation of architecture in jaw cysts and tumors through analysis of heterogeneity, spatial distribution, and texture of voxels. In contrast to traditional qualitative radiological interpretation, this phenotypic approach uses radiomics to classify jaw lesions into three groups: Homogenous with fluid predominance, intermediate with septations, and complex or heterogenous. Radiomic signatures based on texture have proved highly effective and biologically relevant, making them promising tools for imaging biomarkers and decision support in oral and maxillofacial radiology.